Skip to main content

ADMP in PyTorch backend

Project description

DMFF in PyTorch backend

codecov PyPI version

torch version of ADMP is initialized by Zheng Cheng (AISI).

This package implements the PME method (for monopoles) and the QEq method in DMFF with PyTorch, allowing not only GPU-accelerated calculation of PME/QEq methods but also further customization and extension of other PyTorch-based models.

Installation

This package can be installed by:

pip install torch-admp

For the unit tests, you can install the package from source with the following command:

git clone https://github.com/ChiahsinChu/torch-admp
pip install torch-admp[test]
pip install "DMFF @ git+https://github.com/ChiahsinChu/DMFF.git@devel"

Examples

QEq

openmm-torch

from torch_admp.qeq import QEqAllForceModule
from torch_admp.pme import setup_ewald_parameters


kappa, kx, ky, kz = setup_ewald_parameters(rcut, box)
module = QEqAllForceModule(q0, chi, hardness, eta, rcut, kappa, (kx, ky, kz))
jit_module = torch.jit.script(module)
out = jit_module(positions, box)
Notation Description Unit
$q_i$ charge of atom $i$ charge
$z_i$ z-coordinate of atom $i$ length
$L_z$ length of the simulation box in z-direction length
V volume of supercell length $^3$
$\alpha$ Ewald screening parameter length $^{-1}$
$\sigma_i$ Gaussian width of atom $i$ length

Coulomb interaction [ref]

For non-periodic systems, the Coulomb interaction can be calculated directly:

$$ \begin{align} E_{elec}=\sum_i \sum_{j\neq i} \frac{q_i q_j}{r_{ij}}. \end{align} $$

For periodic systems, we consider the Ewald summation under 3D-PBC:

$$ \begin{aligned} E_{elec}=E_{real}+E_{recip}+E_{self}+E_{corr}. \end{aligned} $$

The real space interaction:

$$ \begin{align} E_{real}=\frac{1}{2}\sum_{i,j}q_iq_j \frac{\text{erfc}(\alpha r_{ij})}{r_{ij}}. \end{align} $$

The reciprocal interaction:

$$ \begin{align} E_{recip}=\frac{2\pi}{V}\sum_{k^\prime}\frac{S(k)^2}{k^2}\exp{\left(-\frac{k^2}{4\alpha^2}\right)}, \end{align} $$

where the structural factor $S(k)$ is given by:

$$ \begin{align} S(k)=\sum_i q_i\exp(ik\cdot r_i). \end{align} $$

The self interaction:

$$ \begin{align} E_{self}=-\frac{\alpha}{\sqrt{\pi}}\sum_i q_i^2. \end{align} $$

Non-neutral correction (only with which the energy from 3D Ewald summation is independent with $\alpha$):

$$ \begin{aligned} E=-\frac{\pi}{2V\alpha^2}Q_{tot}^2. \end{aligned} $$

Gaussian damping [ref]

  • DampingForceModule

While the standard Ewald summation is used to calculate the electrostatic interactions between point charges, an additional Gaussian damping term can be applied to adapt the Ewald summation for the Gaussian charges. The damping term is, in fact, a modification of interactions in the real space:

$$ \begin{align} E=-\frac{1}{2}\sum_{i,j}q_iq_j \frac{\text{erfc}(\frac{r_{ij}}{2\sigma_{ij}})}{r_{ij}}+\frac{1}{2\sigma_i\sqrt{\pi}}\sum_i q_i^2, \end{align} $$

where

$$ \sigma_{ij} = \sqrt{\frac{\sigma_i^2 + \sigma_j^2}{2}}. $$

Slab correction [ref]

  • SlabCorrForceModule

When aiming for 2D periodic boundary conditions, the slab correction can be appiled [ref]:

This is done by treating the system as if it were periodic in z, but inserting empty volume between atom slabs and removing dipole inter-slab interactions so that slab-slab interactions are effectively turned off.

The energy for slab correction is given by:

$$ \begin{align} E &= \frac{2\pi}{V} \left( M_z^2 - Q_{\text{tot}} \sum_i q_i z_i^2 + Q_{\text{tot}}^2\frac{L_z^2}{12} \right), \end{align} $$

where

$$ \begin{align} M_z &= \sum_i q_i z_i, \ Q_{\text{tot}} & = \sum_i q_i . \end{align} $$

Unlike lammps, where the empty volume can be inserted internally by the program, the users of this package are expected to insert vacuum with sufficient thickness when building the models to avoid interactions between slabs. Empirically, the thickness of the vacuum is suggested to be twice of the slab thickness.

Chemical interaction [ref]

  • SiteForceModule

In the QEq model, not only the electrostatic interaction but the chemical interaction are considered:

$$ \begin{align} E=\sum_i \chi_i q_i+\frac{1}{2}\sum_i J_iq_i^2. \end{align} $$

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torch_admp-1.1.3.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

torch_admp-1.1.3-py3-none-any.whl (38.5 kB view details)

Uploaded Python 3

File details

Details for the file torch_admp-1.1.3.tar.gz.

File metadata

  • Download URL: torch_admp-1.1.3.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.0

File hashes

Hashes for torch_admp-1.1.3.tar.gz
Algorithm Hash digest
SHA256 583996675f510eca8ae8d584882908b7a88603255ee2529c344374f3898eb482
MD5 4bacb0f201c30448ca45382af56686d2
BLAKE2b-256 b35c7d7fd0bef98ffb68d200d5b63938aabacce52dbac1a5a6e7a3c5820b817e

See more details on using hashes here.

File details

Details for the file torch_admp-1.1.3-py3-none-any.whl.

File metadata

  • Download URL: torch_admp-1.1.3-py3-none-any.whl
  • Upload date:
  • Size: 38.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.0

File hashes

Hashes for torch_admp-1.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 2a2a7c421cd80dec622ca4ec7573cc189a65a0722906569a6f7cd242ce566a96
MD5 3f091da89f241506a1aa648f1b5cf31e
BLAKE2b-256 3a9f619d1a71433d2fca585cdbf4ddb9b49ae0a3de67433112032e978adf88c8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page